import tensorflow as tf

from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt

import sys
import numpy
numpy.set_printoptions(threshold=sys.maxsize)

(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()

# Normalize pixel values to be between 0 and 1
train_images, test_images = train_images / 255.0, test_images / 255.0

class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
               'dog', 'frog', 'horse', 'ship', 'truck']

# data1 = tf.Variable(train_images[1])
# print(data1)
# print(train_labels[1])
# data2 = tf.Variable(train_images[100])
# print(data2)
# print(train_labels[100])
# data3 = tf.Variable(train_images[200])
# print(data3)
# print(train_labels[200])
# data4 = tf.Variable(train_images[300])
# print(data4)
# print(train_labels[300])
# data5 = tf.Variable(train_images[500])
# print(data5)
# print(train_labels[500])

# plt.figure(figsize=(10,10))
# for i in range(25):
#     plt.subplot(5,5,i+1)
#     plt.xticks([])
#     plt.yticks([])
#     plt.grid(False)
#     plt.imshow(train_images[i], cmap=plt.cm.binary)
#     # The CIFAR labels happen to be arrays, 
#     # which is why you need the extra index
#     plt.xlabel(class_names[train_labels[i][0]])
# plt.show()

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))
model.summary()

model.compile(optimizer='adam',
             loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
             metrics=['accuracy'])

history = model.fit(train_images, train_labels, epochs=10,
                    validation_data=(test_images, test_labels))

plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')

# model = tf.keras.models.load_model('./cifar10_cnn_keras_saved_graph')

test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)

print(test_acc)

# for variable in model.trainable_variables:
#   print(variable)

# tf.saved_model.save(model, "./cifar10_cnn_keras_saved_graph")